Too Big To Ignore is a research network and knowledge mobilization partnership established to elevate the profile of small-scale fisheries (SSF), to argue against their marginalization in national and international policies, and to develop research and governance capacity to address global fisheries challenges
Mission
Facts & Figures
[View all]State of the Art
Total annual catch
Source: World Bank/FAO/WorldFish (2010)
Research Highlights
[View all]- GAP recommendations on SSF Guidelines
- Special volume on “Enhancing ecosystem stewardship in small-scale fisheries"
- An article on implementation of the international voluntary guidelines for small-scale fisheries
- E-book: Enhancing Stewardship in Small-Scale Fisheries: Practices and Perspectives
- Community handbook: Small-scale fisheries policy
Member of the Week
[View all]Andrew Song
Andrew Song is an early career social scientist interested in the geography and governance of natural resources with particular reference to small-scale fisheries. He is currently a postdoctoral fellow at the Department of Natural Resource Sciences at McGill University, Canada. [read more]
News & Updates
[View all]Sign up to TBTI new Research Clusters
TBTI is moving into a new phase with the organization of activities around twelve ‘research clusters’, several of which are linked to the original themes, but many are added ba ...[read more]
Information System for Small-scale Fisheries (ISSF) launched
TBTI has recently launched its Information System on Small-scale Fisheries (ISSF), the first interactive global repository of small-scale fisheries, providing information on key ...[read more]
WHO WE ARE
Officially launched in September 2012, this six-year long Social Sciences and Humanities Research Council (SSHRC) project is an open network that encourages anyone interested in SS ...[read more]
Improved data needed on small-scale fisheries
Daniel Pauly and Tony Charles recently wrote a letter in Science about small-scale fisheries data needs. In it, they urge for an improved reporting of catch data that distinguish ...[read more]
